Resampling calibrated adjusted empirical likelihood
Bibliographic record
Abstract
Abstract Empirical‐likelihood‐based inference for parameters defined by the general estimating equations of Qin & Lawless (1994) remains an active research topic. When the sample size is small and/or the dimension of the accompanying estimating equations is high, the resulting confidence regions often have a lower than nominal coverage probability. In addition, the empirical likelihood can be hindered by an empty set problem. The adjusted empirical likelihood (AEL) tackles both problems simultaneously. However, the AEL confidence region with high‐order precision relies on accurate estimation of the required level of adjustment. This has proved difficult, particularly in over‐identified cases. In this article, we show that the general AEL is Bartlett‐correctable and propose a two‐stage procedure for constructing accurate confidence regions. A naive AEL is first employed to address the empty set problem, and it is then Bartlett‐corrected through a resampling procedure. The finite‐sample performance of the proposed method is illustrated by simulations and an example. The Canadian Journal of Statistics 43: 42–59; 2015 © 2014 Statistical Society of Canada
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".